Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,179
2	New Jersey	17,883
3	Massachusetts	13,857
4	Rhode Island	13,814
5	District of Columbia	12,097
6	Connecticut	11,713
7	Delaware	9,484
8	Illinois	9,295
9	Maryland	8,540
10	Louisiana	8,369
11	Nebraska	7,058
12	Iowa	6,028
13	Pennsylvania	5,864
14	Michigan	5,666
15	South Dakota	5,500
16	Indiana	5,109
17	Virginia	4,983
18	Mississippi	4,969
19	Colorado	4,445
20	Minnesota	4,174
21	Georgia	4,133
22	New Mexico	3,573
23	Alabama	3,473
24	Kansas	3,372
25	North Dakota	3,310
26	New Hampshire	3,303
27	Tennessee	3,250
28	Wisconsin	3,054
29	Ohio	2,957
30	Washington	2,903
31	Utah	2,895
32	Nevada	2,719
33	California	2,709
34	North Carolina	2,549
35	Arizona	2,536
36	Florida	2,536
37	Arkansas	2,245
38	Kentucky	2,168
39	South Carolina	2,161
40	Texas	2,140
41	Missouri	2,109
42	Maine	1,655
43	Oklahoma	1,601
44	Idaho	1,569
45	Vermont	1,562
46	Wyoming	1,539
47	Puerto Rico	1,141
48	West Virginia	1,100
49	Oregon	979
50	Alaska	595
51	Montana	461
52	Hawaii	451

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Maryland	184
2	Nebraska	178
3	Rhode Island	133
4	Virginia	124
5	Mississippi	118
6	New Jersey	115
7	Illinois	113
8	Iowa	107
9	Wisconsin	106
10	District of Columbia	96
11	Alabama	93
12	Minnesota	92
13	Massachusetts	87
14	North Carolina	80
15	South Dakota	80
16	Arizona	77
17	Indiana	76
18	New York	76
19	Utah	68
20	Arkansas	65
21	New Hampshire	63
22	California	60
23	Colorado	60
24	Tennessee	60
25	Delaware	58
26	Georgia	57
27	New Mexico	57
28	Pennsylvania	57
29	Kansas	54
30	Michigan	51
31	Texas	51
32	Louisiana	46
33	South Carolina	46
34	North Dakota	44
35	Ohio	44
36	Connecticut	42
37	Kentucky	38
38	Washington	36
39	Florida	34
40	Nevada	34
41	Puerto Rico	33
42	Maine	29
43	Missouri	27
44	Wyoming	23
45	West Virginia	21
46	Idaho	19
47	Oklahoma	16
48	Oregon	12
49	Alaska	9
50	Montana	4
51	Vermont	4
52	Hawaii	1

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,518
2	New Jersey	1,298
3	Connecticut	1,084
4	Massachusetts	974
5	Rhode Island	654
6	District of Columbia	651
7	Louisiana	594
8	Michigan	541
9	Pennsylvania	427
10	Illinois	418
11	Maryland	407
12	Delaware	365
13	Indiana	313
14	Colorado	249
15	Mississippi	238
16	Georgia	183
17	Ohio	182
18	Minnesota	178
19	New Hampshire	175
20	Iowa	166
21	New Mexico	164
22	Virginia	159
23	Washington	147
24	Nevada	134
25	Alabama	124
26	Arizona	121
27	Missouri	120
28	Florida	112
29	California	104
30	Kentucky	97
31	Wisconsin	97
32	South Carolina	93
33	Nebraska	91
34	Vermont	88
35	North Carolina	84
36	Oklahoma	83
37	North Dakota	81
38	Kansas	71
39	South Dakota	66
40	Maine	63
41	Texas	56
42	Tennessee	52
43	Idaho	45
44	Arkansas	43
45	Puerto Rico	41
46	West Virginia	41
47	Oregon	35
48	Utah	33
49	Wyoming	25
50	Montana	15
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	18
2	New Jersey	12
3	Massachusetts	11
4	Connecticut	9
5	District of Columbia	9
6	Illinois	9
7	Delaware	7
8	Maryland	7
9	Pennsylvania	7
10	Mississippi	6
11	Indiana	5
12	Minnesota	5
13	New Hampshire	5
14	New York	5
15	Colorado	4
16	Iowa	4
17	Louisiana	4
18	Michigan	4
19	Virginia	4
20	Arizona	3
21	New Mexico	3
22	North Carolina	3
23	North Dakota	3
24	Ohio	3
25	South Dakota	3
26	Alabama	2
27	California	2
28	Florida	2
29	Georgia	2
30	Kentucky	2
31	Missouri	2
32	Nebraska	2
33	Nevada	2
34	South Carolina	2
35	Wisconsin	2
36	Arkansas	1
37	Maine	1
38	Texas	1
39	Washington	1
40	Wyoming	1
41	Alaska	0
42	Hawaii	0
43	Idaho	0
44	Kansas	0
45	Montana	0
46	Oklahoma	0
47	Oregon	0
48	Puerto Rico	0
49	Tennessee	0
50	Utah	0
51	Vermont	0
52	West Virginia	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,449	1	99
Dakota	Nebraska	82,593	2	99
Lincoln	Arkansas	74,862	3	99
Nobles	Minnesota	69,490	4	99
Lake	Tennessee	58,865	5	99
Davidson	Tennessee	7,506	265	91
Richland	South Carolina	3,514	695	77
Pierce	Washington	2,125	1045	66
Orange	California	1,904	1150	63
York	South Carolina	1,370	1441	54

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Terrell	Georgia	3,048	1	99
Early	Georgia	3,042	2	99
Randolph	Georgia	2,803	3	99
Hancock	Georgia	2,601	4	99
Essex	New Jersey	2,061	5	99
Richland	South Carolina	159	593	81
Pierce	Washington	86	856	72
Davidson	Tennessee	85	865	72
Orange	California	46	1205	61
York	South Carolina	25	1456	53

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.